Traffic sign recognition based on prevailing bag of visual words representation on feature descriptors

Driver Assistance Systems such as traffic sign detection and autonomous car research are largely facilitated with the recent advances on computer vision and pattern recognition. In this work, Bag of visual Words technique has been implemented on Speeded Up Robust Feature (SURF) descriptors of the traffic signs and later the sturdy classifier Support Vector Machine (SVM) is used to categorize the traffic signs to its respective groups. Experimental results demonstrate that the proposed method of implementation can reach an accuracy of 95.2%.